You’re ready to bring AI into your product or platform. Maybe your boss wants a recommendation engine. Maybe the roadmap has a “smart” feature that needs actual intelligence behind it.
Whatever the goal, hold off on hiring the ML engineers for just a bit longer.

If you want to avoid wasting months of work and thousands of dollars, you need to plan better than most companies do.

Goal Setting Comes Before Code

Most teams rush into AI work without a clear idea of what they’re solving. That’s mistake number one.
A goal like “use machine learning” doesn’t help your team. A goal like “automate fraud detection for small transactions” does. It points to a problem, a direction, and a measurable outcome.

Write down what success looks like. Make it short. Make it tied to your business logic. This helps the team stay aligned and gives leadership something to measure against.
If your AI project doesn’t map to cost savings, speed, accuracy, or growth, go back and refine.

Check Your Data Stack

Let’s be real. Most AI projects die because the data isn’t ready.
Before you even start thinking about model training, figure out what data you have, what you need, and what’s missing.
Questions to ask:
Is the data recent and clean?
Are the definitions consistent across teams?
Can your current infrastructure support scale?
Where are the gaps?

Don’t just assume you can fix this later. Model performance means nothing if the input is inconsistent or partial.
If you’re handling sensitive info, look into synthetic data generation to safely expand your datasets. And make sure your storage layer (cloud, data lake, or hybrid) can flex as the project grows.

Build the Roadmap

You won’t ship a perfect AI feature on the first try. That’s fine. What matters is having a plan that breaks the work into manageable pieces.
Start with a small win. Automate a task. Clean up a report. Launch a basic model behind a feature flag. Prove value early and often.
Keep iterating. Tie every phase back to your original goal. That keeps the project aligned and avoids building tech for the sake of it.
Also, expect to spend significant time on data prep. Most of the early effort will be labeling, deduplicating, filling gaps, and building pipelines. This is normal. It is also necessary.

Don’t Skip Data Engineering

You need data engineers early in the process. AI teams move faster when they have clean data pipelines, proper logging, and reproducible workflows.
If you’re missing that foundation, everything will slow down.
Hire engineers who know how to build scalable pipelines, work with cloud systems like GCP or AWS, and automate ingestion and transformation. Python, SQL, and orchestration tools like Airflow go a long way.

Hire With Precision

You don’t always need to hire everyone in-house. For short-term builds or prototyping, external partners or contractors can work well. Just make sure they understand your business and integrate with your existing dev process.
Look for people who have shipped real products, not just demoed models. The flashy portfolio doesn’t matter if they can’t deliver something reliable in production.

Before You Kick Off

Run through this checklist:

• Are your goals clearly tied to business outcomes?
• Do you know which data you need and already have?
• Are data quality standards defined and shared?
• Is there a roadmap with both quick wins and long-term phases?
• Do you have the right engineering support in place?
• Have you decided what to build in-house and what to outsource?

Are stakeholders on board and aligned?

Final Thought

AI can work for your product. It can save time, reduce errors, or unlock new features. But only if the groundwork is done right.
Think about goals. Think about data. Think about team structure.
Then hire. Then build.
The code comes later. Planning comes first.Read the full article here.